Coding Data

Care delivery organizations aspire to increase the content of coded data in their Clinical Information Systems to enhance processes such as: coding for billing purposes; abstracting clinical data for Performance Improvement Efforts; and use in Clinical Decision Support. Unfortunately, care providers find coded data entry cumbersome because it interferes with individualized patient care and workflow. In addition, when compared to traditional natural language narrative, coded entry captures only a fraction of the information produced in the clinical encounter and hence there is a trade-off between sensitivity and specificity in physician coding.

Introduction

There now exist systems which are capable of coding clinical notes. For example, MediClass (a "medical classifier") is a knowledge-based system that automatically classifies the content of clinical notes within an EHR. MediClass is able to code notes by applying a set of application-specific logical rules to the medical concepts that are automatically identified in both the free-text notes and precoded data elements such as medication orders. This system can process data from any EMR system as long as data can be expressed in the Clinical Document Architecture [CDA] data standard that is maintained by Health Level Seven.

The MediClass system was built from open source components and utilizes 3 technologies: Hl7's CDA for representing the clinical encounter including both structured and unstructured data elements.,Natural language processing techniques for parsing and assigning structured semantic representations to text segments within the CDA, and knowledge-based systems for processing semantic representations addressing specific subdomains of medicine and clinical care and for defining logical classifications over the semantic contents of a clinical note. Studies have shown the application to have be similar in accuracy to trained human medical record abstractors; using the trained abstractors as gold standard, the system performed with an average sensitivity of 82% and an average specificity of 93%.

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